12 May 2004 Segmentation of the left ventricle in 4D-dSPECT data using free-form deformation of super quadrics
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Abstract
In dynamic SPECT (dSPECT) images, function of a particular organ may be analyzed by measuring the temporal change of the spatial distribution of radioactive tracer. The organ-specific and location-specific time-activity curves (TAC) of the different heart regions (regions with normal blood circulation and with disturbed blood circulation) are helpful for the diagnosis of heart diseases.A problem of the derivation of the TACs is that the dSPECT images have a poor spatial and temporal resolution and the data is distorted because noise effects, partial volume effects and scatter artifacts. Therefore in a preprocessing step the quality of the data is improved with a nonlinear isotropic diffusion in combination with the principal component analysis. Segmentation according to some homogeneity principle will deliver regions of similar functional behavior but the segmented regions do not directly point to anatomy. For our goal of anatomy specific segmentation information about anatomy is provided a-priori and it must be fitted to the data. For initialization the user have to mark six positions of the left ventricle in the data set which are used to place a super ellipsoid. The parameters of this super ellipsoid are obtained from the computed mean shape of six manual segmented left ventricles in test data sets. A closer fit to the high gradients of the boundaries of the heart wall is achieved using the free form deformation method. For evaluation segmentation results are compared with a manual segmentation. In all test images we could ascertain a good correspondence between the manual and automatic segmentation.
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Regina Pohle, Regina Pohle, Melanie Wegner, Melanie Wegner, Karsten Rink, Karsten Rink, Klaus D. Toennies, Klaus D. Toennies, Anna Celler, Anna Celler, Stephan Blinder, Stephan Blinder, } "Segmentation of the left ventricle in 4D-dSPECT data using free-form deformation of super quadrics", Proc. SPIE 5370, Medical Imaging 2004: Image Processing, (12 May 2004); doi: 10.1117/12.535128; https://doi.org/10.1117/12.535128
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